Overview

Dataset statistics

Number of variables23
Number of observations53257
Missing cells0
Missing cells (%)0.0%
Total size in memory9.3 MiB
Average record size in memory184.0 B

Variable types

Numeric21
Categorical2

Alerts

sum_services is highly correlated with services_frequency and 3 other fieldsHigh correlation
services_frequency is highly correlated with sum_services and 3 other fieldsHigh correlation
inter_avulsa is highly correlated with sum_services and 1 other fieldsHigh correlation
prezao_diario is highly correlated with sum_services and 1 other fieldsHigh correlation
prezao_semanal is highly correlated with sum_services and 1 other fieldsHigh correlation
sum_services is highly correlated with services_frequency and 2 other fieldsHigh correlation
services_frequency is highly correlated with sum_services and 2 other fieldsHigh correlation
inter_avulsa is highly correlated with sum_services and 1 other fieldsHigh correlation
prezao_diario is highly correlated with services_frequencyHigh correlation
prezao_semanal is highly correlated with sum_servicesHigh correlation
sum_services is highly correlated with services_frequency and 3 other fieldsHigh correlation
services_frequency is highly correlated with sum_services and 3 other fieldsHigh correlation
inter_avulsa is highly correlated with sum_services and 1 other fieldsHigh correlation
prezao_diario is highly correlated with sum_services and 1 other fieldsHigh correlation
prezao_semanal is highly correlated with sum_services and 1 other fieldsHigh correlation
sum_services is highly correlated with services_frequency and 2 other fieldsHigh correlation
services_frequency is highly correlated with sum_services and 3 other fieldsHigh correlation
inter_avulsa is highly correlated with sum_services and 1 other fieldsHigh correlation
clube is highly correlated with pct_internet_mensalHigh correlation
entretenimento is highly correlated with prezao_mensal and 2 other fieldsHigh correlation
pct_internet_mensal is highly correlated with clubeHigh correlation
prezao_diario is highly correlated with services_frequencyHigh correlation
prezao_mensal is highly correlated with entretenimentoHigh correlation
prezao_semanal is highly correlated with sum_servicesHigh correlation
servicos_operadora is highly correlated with services_frequencyHigh correlation
sms_internacional is highly correlated with entretenimento and 1 other fieldsHigh correlation
transf_entre_regionais is highly correlated with entretenimento and 1 other fieldsHigh correlation
antivirus is highly skewed (γ1 = 62.88240218) Skewed
app_educacao is highly skewed (γ1 = 34.263001) Skewed
app_emprego is highly skewed (γ1 = 52.74311433) Skewed
app_saude is highly skewed (γ1 = 59.17882664) Skewed
clube is highly skewed (γ1 = 86.01255931) Skewed
pre_mix_giga is highly skewed (γ1 = 32.65975538) Skewed
games is highly skewed (γ1 = 32.01856606) Skewed
prezao_quinzenal is highly skewed (γ1 = 36.25463458) Skewed
recarga_sos is highly skewed (γ1 = 43.45335845) Skewed
sms_cobrar is highly skewed (γ1 = 94.98582431) Skewed
sms_internacional is highly skewed (γ1 = 39.17476276) Skewed
truecaller is highly skewed (γ1 = 28.81193418) Skewed
sum_services has 35757 (67.1%) zeros Zeros
services_frequency has 35757 (67.1%) zeros Zeros
inter_avulsa has 44038 (82.7%) zeros Zeros
antivirus has 53171 (99.8%) zeros Zeros
app_educacao has 53137 (99.8%) zeros Zeros
app_emprego has 53184 (99.9%) zeros Zeros
app_saude has 53194 (99.9%) zeros Zeros
clube has 50012 (93.9%) zeros Zeros
pre_mix_giga has 53119 (99.7%) zeros Zeros
entretenimento has 51412 (96.5%) zeros Zeros
games has 53014 (99.5%) zeros Zeros
pct_internet_mensal has 52386 (98.4%) zeros Zeros
prezao_diario has 45537 (85.5%) zeros Zeros
prezao_mensal has 51247 (96.2%) zeros Zeros
prezao_quinzenal has 53176 (99.8%) zeros Zeros
prezao_semanal has 44772 (84.1%) zeros Zeros
recarga_sos has 53175 (99.8%) zeros Zeros
servicos_operadora has 48928 (91.9%) zeros Zeros
sms_cobrar has 53056 (99.6%) zeros Zeros
sms_internacional has 52978 (99.5%) zeros Zeros
truecaller has 53055 (99.6%) zeros Zeros

Reproduction

Analysis started2022-03-15 15:21:34.492623
Analysis finished2022-03-15 15:22:14.414105
Duration39.92 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

sum_services
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8251
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.21653492
Minimum0
Maximum1202.26
Zeros35757
Zeros (%)67.1%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:14.468925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314.99
95-th percentile127.344
Maximum1202.26
Range1202.26
Interquartile range (IQR)14.99

Descriptive statistics

Standard deviation49.52950157
Coefficient of variation (CV)2.334476471
Kurtosis27.03717219
Mean21.21653492
Median Absolute Deviation (MAD)0
Skewness3.940826201
Sum1129929
Variance2453.171525
MonotonicityNot monotonic
2022-03-15T12:22:14.552645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035757
67.1%
9.99431
 
0.8%
19.98224
 
0.4%
19.99166
 
0.3%
29.97119
 
0.2%
1.39100
 
0.2%
1.9994
 
0.2%
1.4984
 
0.2%
0.9976
 
0.1%
39.9673
 
0.1%
Other values (8241)16133
30.3%
ValueCountFrequency (%)
035757
67.1%
0.0122
 
< 0.1%
0.0231
 
0.1%
0.0318
 
< 0.1%
0.0430
 
0.1%
0.0523
 
< 0.1%
0.0616
 
< 0.1%
0.0721
 
< 0.1%
0.088
 
< 0.1%
0.0912
 
< 0.1%
ValueCountFrequency (%)
1202.261
< 0.1%
885.591
< 0.1%
772.471
< 0.1%
691.631
< 0.1%
671.961
< 0.1%
669.541
< 0.1%
647.071
< 0.1%
632.841
< 0.1%
629.581
< 0.1%
6111
< 0.1%

services_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct174
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.78787765
Minimum0
Maximum448
Zeros35757
Zeros (%)67.1%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:14.639356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile32
Maximum448
Range448
Interquartile range (IQR)4

Descriptive statistics

Standard deviation14.74731503
Coefficient of variation (CV)2.547965925
Kurtosis51.77253174
Mean5.78787765
Median Absolute Deviation (MAD)0
Skewness5.237101502
Sum308245
Variance217.4833005
MonotonicityNot monotonic
2022-03-15T12:22:14.714102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035757
67.1%
11883
 
3.5%
21068
 
2.0%
3989
 
1.9%
4832
 
1.6%
5709
 
1.3%
6690
 
1.3%
7650
 
1.2%
8603
 
1.1%
13543
 
1.0%
Other values (164)9533
 
17.9%
ValueCountFrequency (%)
035757
67.1%
11883
 
3.5%
21068
 
2.0%
3989
 
1.9%
4832
 
1.6%
5709
 
1.3%
6690
 
1.3%
7650
 
1.2%
8603
 
1.1%
9525
 
1.0%
ValueCountFrequency (%)
4481
< 0.1%
3281
< 0.1%
2581
< 0.1%
2431
< 0.1%
2331
< 0.1%
2321
< 0.1%
2311
< 0.1%
2241
< 0.1%
2191
< 0.1%
2161
< 0.1%

inter_avulsa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct157
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.609872881
Minimum0
Maximum447
Zeros44038
Zeros (%)82.7%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:14.792842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum447
Range447
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.75048106
Coefficient of variation (CV)4.119158884
Kurtosis123.5536249
Mean2.609872881
Median Absolute Deviation (MAD)0
Skewness8.256024778
Sum138994
Variance115.572843
MonotonicityNot monotonic
2022-03-15T12:22:14.867589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044038
82.7%
11501
 
2.8%
2877
 
1.6%
3654
 
1.2%
4554
 
1.0%
5439
 
0.8%
6423
 
0.8%
7357
 
0.7%
8311
 
0.6%
9303
 
0.6%
Other values (147)3800
 
7.1%
ValueCountFrequency (%)
044038
82.7%
11501
 
2.8%
2877
 
1.6%
3654
 
1.2%
4554
 
1.0%
5439
 
0.8%
6423
 
0.8%
7357
 
0.7%
8311
 
0.6%
9303
 
0.6%
ValueCountFrequency (%)
4471
< 0.1%
2191
< 0.1%
2151
< 0.1%
2111
< 0.1%
2051
< 0.1%
2001
< 0.1%
1971
< 0.1%
1841
< 0.1%
1811
< 0.1%
1761
< 0.1%

antivirus
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004975871716
Minimum0
Maximum21
Zeros53171
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:14.939349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1795920193
Coefficient of variation (CV)36.09257424
Kurtosis5328.213576
Mean0.004975871716
Median Absolute Deviation (MAD)0
Skewness62.88240218
Sum265
Variance0.03225329341
MonotonicityNot monotonic
2022-03-15T12:22:14.998152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
053171
99.8%
136
 
0.1%
214
 
< 0.1%
311
 
< 0.1%
59
 
< 0.1%
47
 
< 0.1%
63
 
< 0.1%
122
 
< 0.1%
211
 
< 0.1%
81
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
053171
99.8%
136
 
0.1%
214
 
< 0.1%
311
 
< 0.1%
47
 
< 0.1%
59
 
< 0.1%
63
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
122
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
111
 
< 0.1%
81
 
< 0.1%
63
 
< 0.1%
59
< 0.1%
47
< 0.1%
311
< 0.1%
214
< 0.1%

app_educacao
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005895938562
Minimum0
Maximum10
Zeros53137
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:15.054965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1535805613
Coefficient of variation (CV)26.04853488
Kurtosis1382.628972
Mean0.005895938562
Median Absolute Deviation (MAD)0
Skewness34.263001
Sum314
Variance0.0235869888
MonotonicityNot monotonic
2022-03-15T12:22:15.116755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
053137
99.8%
147
 
0.1%
228
 
0.1%
312
 
< 0.1%
412
 
< 0.1%
68
 
< 0.1%
58
 
< 0.1%
73
 
< 0.1%
101
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
053137
99.8%
147
 
0.1%
228
 
0.1%
312
 
< 0.1%
412
 
< 0.1%
58
 
< 0.1%
68
 
< 0.1%
73
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
81
 
< 0.1%
73
 
< 0.1%
68
 
< 0.1%
58
 
< 0.1%
412
 
< 0.1%
312
 
< 0.1%
228
 
0.1%
147
 
0.1%
053137
99.8%

app_emprego
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00486321047
Minimum0
Maximum16
Zeros53184
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:15.175558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1799084914
Coefficient of variation (CV)36.99377037
Kurtosis3337.2311
Mean0.00486321047
Median Absolute Deviation (MAD)0
Skewness52.74311433
Sum259
Variance0.03236706528
MonotonicityNot monotonic
2022-03-15T12:22:15.235359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
053184
99.9%
128
 
0.1%
210
 
< 0.1%
39
 
< 0.1%
47
 
< 0.1%
74
 
< 0.1%
54
 
< 0.1%
63
 
< 0.1%
132
 
< 0.1%
102
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
053184
99.9%
128
 
0.1%
210
 
< 0.1%
39
 
< 0.1%
47
 
< 0.1%
54
 
< 0.1%
63
 
< 0.1%
74
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
132
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
91
 
< 0.1%
81
 
< 0.1%
74
< 0.1%
63
< 0.1%
54
< 0.1%
47
< 0.1%

app_saude
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003530052387
Minimum0
Maximum13
Zeros53194
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:15.296154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1444569656
Coefficient of variation (CV)40.92204582
Kurtosis4092.201016
Mean0.003530052387
Median Absolute Deviation (MAD)0
Skewness59.17882664
Sum188
Variance0.0208678149
MonotonicityNot monotonic
2022-03-15T12:22:15.360938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
053194
99.9%
129
 
0.1%
212
 
< 0.1%
35
 
< 0.1%
45
 
< 0.1%
83
 
< 0.1%
62
 
< 0.1%
52
 
< 0.1%
92
 
< 0.1%
131
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
053194
99.9%
129
 
0.1%
212
 
< 0.1%
35
 
< 0.1%
45
 
< 0.1%
52
 
< 0.1%
62
 
< 0.1%
83
 
< 0.1%
92
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
92
 
< 0.1%
83
 
< 0.1%
62
 
< 0.1%
52
 
< 0.1%
45
< 0.1%
35
< 0.1%
212
< 0.1%

clube
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1171864731
Minimum0
Maximum155
Zeros50012
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:15.427716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum155
Range155
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9705328638
Coefficient of variation (CV)8.281953009
Kurtosis12652.09812
Mean0.1171864731
Median Absolute Deviation (MAD)0
Skewness86.01255931
Sum6241
Variance0.9419340398
MonotonicityNot monotonic
2022-03-15T12:22:15.494493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
050012
93.9%
12065
 
3.9%
2623
 
1.2%
3242
 
0.5%
497
 
0.2%
566
 
0.1%
656
 
0.1%
731
 
0.1%
917
 
< 0.1%
129
 
< 0.1%
Other values (12)39
 
0.1%
ValueCountFrequency (%)
050012
93.9%
12065
 
3.9%
2623
 
1.2%
3242
 
0.5%
497
 
0.2%
566
 
0.1%
656
 
0.1%
731
 
0.1%
88
 
< 0.1%
917
 
< 0.1%
ValueCountFrequency (%)
1551
 
< 0.1%
671
 
< 0.1%
291
 
< 0.1%
261
 
< 0.1%
201
 
< 0.1%
161
 
< 0.1%
152
 
< 0.1%
143
 
< 0.1%
135
< 0.1%
129
< 0.1%

pre_mix_giga
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02164973618
Minimum0
Maximum43
Zeros53119
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:15.565255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5053796119
Coefficient of variation (CV)23.34345359
Kurtosis1570.048202
Mean0.02164973618
Median Absolute Deviation (MAD)0
Skewness32.65975538
Sum1153
Variance0.2554085522
MonotonicityNot monotonic
2022-03-15T12:22:15.627052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
053119
99.7%
1319
 
< 0.1%
1212
 
< 0.1%
1011
 
< 0.1%
611
 
< 0.1%
511
 
< 0.1%
1111
 
< 0.1%
411
 
< 0.1%
210
 
< 0.1%
78
 
< 0.1%
Other values (9)34
 
0.1%
ValueCountFrequency (%)
053119
99.7%
18
 
< 0.1%
210
 
< 0.1%
37
 
< 0.1%
411
 
< 0.1%
511
 
< 0.1%
611
 
< 0.1%
78
 
< 0.1%
84
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
431
 
< 0.1%
271
 
< 0.1%
172
 
< 0.1%
151
 
< 0.1%
144
 
< 0.1%
1319
< 0.1%
1212
< 0.1%
1111
< 0.1%
1011
< 0.1%
96
 
< 0.1%

entretenimento
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.184182361
Minimum0
Maximum40
Zeros51412
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:15.697816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.329913995
Coefficient of variation (CV)7.220637132
Kurtosis142.6900633
Mean0.184182361
Median Absolute Deviation (MAD)0
Skewness10.44025972
Sum9809
Variance1.768671234
MonotonicityNot monotonic
2022-03-15T12:22:15.773562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
051412
96.5%
1424
 
0.8%
2267
 
0.5%
3203
 
0.4%
4173
 
0.3%
5126
 
0.2%
699
 
0.2%
782
 
0.2%
1376
 
0.1%
871
 
0.1%
Other values (22)324
 
0.6%
ValueCountFrequency (%)
051412
96.5%
1424
 
0.8%
2267
 
0.5%
3203
 
0.4%
4173
 
0.3%
5126
 
0.2%
699
 
0.2%
782
 
0.2%
871
 
0.1%
960
 
0.1%
ValueCountFrequency (%)
401
 
< 0.1%
381
 
< 0.1%
341
 
< 0.1%
291
 
< 0.1%
272
 
< 0.1%
263
< 0.1%
253
< 0.1%
246
< 0.1%
232
 
< 0.1%
222
 
< 0.1%

games
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01122857089
Minimum0
Maximum15
Zeros53014
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:15.839342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2281986149
Coefficient of variation (CV)20.32303283
Kurtosis1302.414645
Mean0.01122857089
Median Absolute Deviation (MAD)0
Skewness32.01856606
Sum598
Variance0.05207460783
MonotonicityNot monotonic
2022-03-15T12:22:15.895156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
053014
99.5%
1121
 
0.2%
246
 
0.1%
330
 
0.1%
415
 
< 0.1%
58
 
< 0.1%
66
 
< 0.1%
85
 
< 0.1%
103
 
< 0.1%
113
 
< 0.1%
Other values (3)6
 
< 0.1%
ValueCountFrequency (%)
053014
99.5%
1121
 
0.2%
246
 
0.1%
330
 
0.1%
415
 
< 0.1%
58
 
< 0.1%
66
 
< 0.1%
72
 
< 0.1%
85
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
151
 
< 0.1%
113
 
< 0.1%
103
 
< 0.1%
93
 
< 0.1%
85
 
< 0.1%
72
 
< 0.1%
66
 
< 0.1%
58
 
< 0.1%
415
< 0.1%
330
0.1%

pct_internet_mensal
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02923559344
Minimum0
Maximum15
Zeros52386
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:15.954956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3036221726
Coefficient of variation (CV)10.38536034
Kurtosis532.14907
Mean0.02923559344
Median Absolute Deviation (MAD)0
Skewness18.97331516
Sum1557
Variance0.09218642371
MonotonicityNot monotonic
2022-03-15T12:22:16.011765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
052386
98.4%
1563
 
1.1%
2146
 
0.3%
387
 
0.2%
429
 
0.1%
516
 
< 0.1%
69
 
< 0.1%
88
 
< 0.1%
74
 
< 0.1%
93
 
< 0.1%
Other values (5)6
 
< 0.1%
ValueCountFrequency (%)
052386
98.4%
1563
 
1.1%
2146
 
0.3%
387
 
0.2%
429
 
0.1%
516
 
< 0.1%
69
 
< 0.1%
74
 
< 0.1%
88
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
151
 
< 0.1%
141
 
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
93
 
< 0.1%
88
< 0.1%
74
 
< 0.1%
69
< 0.1%
516
< 0.1%

prezao_diario
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct98
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.436731322
Minimum0
Maximum134
Zeros45537
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:16.086511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum134
Range134
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.761116095
Coefficient of variation (CV)4.009877148
Kurtosis77.48456073
Mean1.436731322
Median Absolute Deviation (MAD)0
Skewness7.360936643
Sum76516
Variance33.19045866
MonotonicityNot monotonic
2022-03-15T12:22:16.169235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045537
85.5%
11083
 
2.0%
2831
 
1.6%
3688
 
1.3%
4548
 
1.0%
5543
 
1.0%
6433
 
0.8%
7389
 
0.7%
8321
 
0.6%
9276
 
0.5%
Other values (88)2608
 
4.9%
ValueCountFrequency (%)
045537
85.5%
11083
 
2.0%
2831
 
1.6%
3688
 
1.3%
4548
 
1.0%
5543
 
1.0%
6433
 
0.8%
7389
 
0.7%
8321
 
0.6%
9276
 
0.5%
ValueCountFrequency (%)
1341
< 0.1%
1301
< 0.1%
1091
< 0.1%
1051
< 0.1%
1031
< 0.1%
1021
< 0.1%
971
< 0.1%
962
< 0.1%
921
< 0.1%
912
< 0.1%

prezao_mensal
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07814935126
Minimum0
Maximum18
Zeros51247
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:16.649628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4624381051
Coefficient of variation (CV)5.917363326
Kurtosis109.0219446
Mean0.07814935126
Median Absolute Deviation (MAD)0
Skewness8.296861273
Sum4162
Variance0.2138490011
MonotonicityNot monotonic
2022-03-15T12:22:16.708431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
051247
96.2%
1815
 
1.5%
2557
 
1.0%
3471
 
0.9%
490
 
0.2%
541
 
0.1%
620
 
< 0.1%
77
 
< 0.1%
84
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
051247
96.2%
1815
 
1.5%
2557
 
1.0%
3471
 
0.9%
490
 
0.2%
541
 
0.1%
620
 
< 0.1%
77
 
< 0.1%
84
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
181
 
< 0.1%
94
 
< 0.1%
84
 
< 0.1%
77
 
< 0.1%
620
 
< 0.1%
541
 
0.1%
490
 
0.2%
3471
0.9%
2557
1.0%
1815
1.5%

prezao_quinzenal
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006665790413
Minimum0
Maximum14
Zeros53176
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:16.768231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1962274532
Coefficient of variation (CV)29.43798725
Kurtosis1591.041549
Mean0.006665790413
Median Absolute Deviation (MAD)0
Skewness36.25463458
Sum355
Variance0.03850521339
MonotonicityNot monotonic
2022-03-15T12:22:16.823048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
053176
99.8%
317
 
< 0.1%
615
 
< 0.1%
112
 
< 0.1%
510
 
< 0.1%
79
 
< 0.1%
49
 
< 0.1%
25
 
< 0.1%
82
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
053176
99.8%
112
 
< 0.1%
25
 
< 0.1%
317
 
< 0.1%
49
 
< 0.1%
510
 
< 0.1%
615
 
< 0.1%
79
 
< 0.1%
82
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
131
 
< 0.1%
82
 
< 0.1%
79
< 0.1%
615
< 0.1%
510
< 0.1%
49
< 0.1%
317
< 0.1%
25
 
< 0.1%
112
< 0.1%

prezao_semanal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9513866722
Minimum0
Maximum39
Zeros44772
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:16.892815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.929505552
Coefficient of variation (CV)3.079195492
Kurtosis19.98536558
Mean0.9513866722
Median Absolute Deviation (MAD)0
Skewness4.036229132
Sum50668
Variance8.582002781
MonotonicityNot monotonic
2022-03-15T12:22:16.964575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
044772
84.1%
11566
 
2.9%
21127
 
2.1%
3858
 
1.6%
4684
 
1.3%
5590
 
1.1%
13503
 
0.9%
6484
 
0.9%
7415
 
0.8%
12402
 
0.8%
Other values (27)1856
 
3.5%
ValueCountFrequency (%)
044772
84.1%
11566
 
2.9%
21127
 
2.1%
3858
 
1.6%
4684
 
1.3%
5590
 
1.1%
6484
 
0.9%
7415
 
0.8%
8384
 
0.7%
9338
 
0.6%
ValueCountFrequency (%)
391
 
< 0.1%
381
 
< 0.1%
372
 
< 0.1%
362
 
< 0.1%
321
 
< 0.1%
311
 
< 0.1%
307
< 0.1%
298
< 0.1%
286
< 0.1%
276
< 0.1%

recarga_sos
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003492498639
Minimum0
Maximum8
Zeros53175
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:17.030354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1102517361
Coefficient of variation (CV)31.56815434
Kurtosis2256.373371
Mean0.003492498639
Median Absolute Deviation (MAD)0
Skewness43.45335845
Sum186
Variance0.01215544531
MonotonicityNot monotonic
2022-03-15T12:22:17.088161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
053175
99.8%
134
 
0.1%
227
 
0.1%
46
 
< 0.1%
36
 
< 0.1%
64
 
< 0.1%
52
 
< 0.1%
72
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
053175
99.8%
134
 
0.1%
227
 
0.1%
36
 
< 0.1%
46
 
< 0.1%
52
 
< 0.1%
64
 
< 0.1%
72
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
72
 
< 0.1%
64
 
< 0.1%
52
 
< 0.1%
46
 
< 0.1%
36
 
< 0.1%
227
 
0.1%
134
 
0.1%
053175
99.8%

servicos_operadora
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2152580881
Minimum0
Maximum36
Zeros48928
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:17.153941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.289220813
Coefficient of variation (CV)5.989186396
Kurtosis145.331566
Mean0.2152580881
Median Absolute Deviation (MAD)0
Skewness10.61935063
Sum11464
Variance1.662090305
MonotonicityNot monotonic
2022-03-15T12:22:17.222711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
048928
91.9%
12947
 
5.5%
2455
 
0.9%
3181
 
0.3%
4116
 
0.2%
572
 
0.1%
866
 
0.1%
1264
 
0.1%
1362
 
0.1%
761
 
0.1%
Other values (22)305
 
0.6%
ValueCountFrequency (%)
048928
91.9%
12947
 
5.5%
2455
 
0.9%
3181
 
0.3%
4116
 
0.2%
572
 
0.1%
656
 
0.1%
761
 
0.1%
866
 
0.1%
947
 
0.1%
ValueCountFrequency (%)
361
 
< 0.1%
351
 
< 0.1%
312
 
< 0.1%
301
 
< 0.1%
282
 
< 0.1%
271
 
< 0.1%
252
 
< 0.1%
242
 
< 0.1%
231
 
< 0.1%
226
< 0.1%

sms_cobrar
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01462718516
Minimum0
Maximum79
Zeros53056
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:17.290485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum79
Range79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6230513474
Coefficient of variation (CV)42.59543724
Kurtosis10808.16532
Mean0.01462718516
Median Absolute Deviation (MAD)0
Skewness94.98582431
Sum779
Variance0.3881929815
MonotonicityNot monotonic
2022-03-15T12:22:17.351282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
053056
99.6%
1120
 
0.2%
227
 
0.1%
311
 
< 0.1%
49
 
< 0.1%
69
 
< 0.1%
55
 
< 0.1%
84
 
< 0.1%
73
 
< 0.1%
792
 
< 0.1%
Other values (9)11
 
< 0.1%
ValueCountFrequency (%)
053056
99.6%
1120
 
0.2%
227
 
0.1%
311
 
< 0.1%
49
 
< 0.1%
55
 
< 0.1%
69
 
< 0.1%
73
 
< 0.1%
84
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
792
< 0.1%
491
< 0.1%
331
< 0.1%
292
< 0.1%
221
< 0.1%
172
< 0.1%
151
< 0.1%
141
< 0.1%
121
< 0.1%
91
< 0.1%

sms_internacional
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008562254727
Minimum0
Maximum14
Zeros52978
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:17.412079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1589840962
Coefficient of variation (CV)18.56801757
Kurtosis2446.919821
Mean0.008562254727
Median Absolute Deviation (MAD)0
Skewness39.17476276
Sum456
Variance0.02527594284
MonotonicityNot monotonic
2022-03-15T12:22:17.465899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
052978
99.5%
1190
 
0.4%
256
 
0.1%
313
 
< 0.1%
49
 
< 0.1%
55
 
< 0.1%
62
 
< 0.1%
131
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
052978
99.5%
1190
 
0.4%
256
 
0.1%
313
 
< 0.1%
49
 
< 0.1%
55
 
< 0.1%
62
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
131
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
62
 
< 0.1%
55
 
< 0.1%
49
 
< 0.1%
313
 
< 0.1%
256
 
0.1%
1190
0.4%

transf_entre_regionais
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size416.2 KiB
53118 
 
138
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Common Values

ValueCountFrequency (%)
53118
99.7%
138
 
0.3%
1
 
< 0.1%

Length

2022-03-15T12:22:17.528689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T12:22:17.568556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
53118
99.7%
138
 
0.3%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

truecaller
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008299378485
Minimum0
Maximum9
Zeros53055
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size416.2 KiB
2022-03-15T12:22:17.610415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1703990048
Coefficient of variation (CV)20.53153801
Kurtosis1010.618403
Mean0.008299378485
Median Absolute Deviation (MAD)0
Skewness28.81193418
Sum442
Variance0.02903582084
MonotonicityNot monotonic
2022-03-15T12:22:17.669219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
053055
99.6%
1105
 
0.2%
237
 
0.1%
322
 
< 0.1%
417
 
< 0.1%
59
 
< 0.1%
65
 
< 0.1%
74
 
< 0.1%
92
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
053055
99.6%
1105
 
0.2%
237
 
0.1%
322
 
< 0.1%
417
 
< 0.1%
59
 
< 0.1%
65
 
< 0.1%
74
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
81
 
< 0.1%
74
 
< 0.1%
65
 
< 0.1%
59
 
< 0.1%
417
 
< 0.1%
322
 
< 0.1%
237
 
0.1%
1105
 
0.2%
053055
99.6%

venda
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size416.2 KiB
40490 
12767 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Common Values

ValueCountFrequency (%)
40490
76.0%
12767
 
24.0%

Length

2022-03-15T12:22:17.729024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-15T12:22:17.769886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
40490
76.0%
12767
 
24.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-15T12:22:12.265296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:37.150737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:38.853043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:40.598206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:42.333403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:43.984875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:45.762935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:47.496132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:49.141633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:51.024337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:52.628970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-15T12:21:44.281887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-15T12:22:06.501569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:08.545736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:10.253026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:11.942376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:13.636707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:38.613844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:40.360999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:41.985566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:43.756643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:45.382202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:47.253946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:48.904426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:50.778160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:52.403723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:54.101046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:56.001691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:57.696023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:59.431216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:01.464419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:03.152769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:04.870029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:06.584292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:08.628455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:10.336746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:12.027092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:13.716439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:38.694573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:40.444720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:42.183903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:43.836376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:45.612432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:47.339659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:48.986153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:50.861880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:52.482456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:54.184764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:56.084414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:57.782732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:59.518923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:01.549136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:03.235496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:04.952752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:06.667019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:08.710186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:10.421462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:12.110813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:13.793185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:38.777296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:40.523456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:42.262636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:43.913120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:45.692171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:47.420390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:49.067880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:50.948585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:52.559203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:54.267489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:56.165143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:57.865456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:21:59.607626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:01.633852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:03.318219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:05.037469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:06.747749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:08.790915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:10.503189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-15T12:22:12.190546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-03-15T12:22:17.830682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-15T12:22:17.999116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-15T12:22:18.168549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-15T12:22:18.309079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-15T12:22:18.395788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-15T12:22:13.940693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-15T12:22:14.251652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.